A Panoramic View of Socio-Cultural Sensitivity in Digital Technologies: A Comprehensive Review and Future Directions
Bibliographic record
Abstract
In recent years, there has been growing research interest in aligning technological designs with users’ lived experiences. The goal of this work is to review existing work on the current state-of-the-art on incorporating individual and community values and beliefs into various kinds of interactive computerized technologies, emphasizing socio-cultural sensitivity. After screening 235 records, 45 papers were included in this review. Our research reveals that researchers are at the forefront of developing advanced socio-cultural digital tools and interactive educational platforms. They frequently employ techniques like collaborative dialogue facilitation, personalized linguistic support, and the integration of culturally significant design principles, such as cultural narratives and symbols. This commitment to technology’s transformative potential extends beyond education, making its mark in healthcare, social networks, finance, and other domains. We conclude by providing an overview of the questions that other researchers can investigate in the future for designing technologies that are socio-culturally sensitive. Future studies would benefit from a wider use of theories to account for the complexity of human behavior while designing socio-culturally sensitive technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".